1. Field of the Present Invention
The present invention relates generally to the field of image processing, and more specifically to the field of de-noising and segmenting coherent images.
2. History of the Related Art
Coherent imaging has a number of practical uses, for example in synthetic aperture radar (SAR) and ultrasonic imaging. For example, SAR has a number of advantages over other passive imaging systems because, as the SAR system emits its own radiation, it is not dependent upon any external source of radiation. Moreover, due to the long wavelengths, most SAR systems are capable of imaging the Earth's surface independent of inclement or adverse weather.
Unfortunately, the efficiency of aerial data collection and visualization with SAR systems is often impeded by their high susceptibility to speckle noise. A SAR system measures both the amplitude and the phase of the signals echoed from the Earth's surface. Due to the microscopic roughness of the reflecting objects on the surface, the amplitudes of the echoed signals reflected from the locality of each targeted spot have random phases. The amplitudes of these signals interfere coherently at the antenna, which ultimately gives rise to the signal-dependent and grainy speckle noise formed in the SAR imagery. Similarly, speckle noise in ultrasonic imaging is caused by the interference of energy from randomly distributed scatters, too small to be resolved by the imaging system. Speckle noise degrades both the spatial and contrast resolution in ultrasonic imaging and thereby reduces the diagnostic value of the images.
There have been a number of speckle noise reduction techniques developed in the image processing field. Some example techniques include the Lee filter and its derivatives, the geometric filter, the Kuan filter, the Frost filter and its derivatives, the Gamma MAP filter, the wavelet approach and some other Markov-based techniques. Unfortunately, each of these approaches assumes that speckle noise is multiplicative relative to the image intensity. While this assumption can be useful in simplifying the complex nature of speckle noise, it does not allow any of the foregoing techniques to substantially eradicate speckle noise from an image.
Ultrasound medical imagery is considered as one of the primary means for imaging organs and tissues. The success of the technique is due to near zero risk for patients and its low cost. By using ultrasound imagery, clinicians avoid unnecessary, instrusive, risky and expensive surgeries to the patients. Unfortunately, speckle noise is an inherent component of any ultrasound medical imaging because of the interference of energy from randomly distributed scatters (e.g., blood and tissue) of ultrasonic waves that are too small to be resolved by the imaging system. In the medical field, speckle noise is typically referred to as “texture” and it generally reduces the image resolution and contrast due to its granular appearance, which can make both visual and automated imaging interpretation difficult. As unreliable medical images can have catastrophic consequences, there is a need in the medical imaging arts to provide ultrasound images with reduced speckle noise.
Similarly, image segmentation is often used in the automated analysis and interpretation of SAR and ultrasound data. Various segmentation approaches have been attempted in the past, such as for example edge detection, region growing technique and thresholding technique. As in the case of speckle noise, each of these techniques is fundamentally flawed in that they either require affirmative user input to segment the image and/or they are adversely affected by the speckle noise otherwise inherent in SAR images and ultrasound images. As such, there is a need in the art of image processing for one or more methods, systems and/or devices for reducing speckle noise in an image as well as segmenting the same image for ease of analysis and interpretation of both SAR and ultrasound data.